Go to DBLP homepageDynamic Computation Offloading in Mobile-Edge-Cloud Computing Systems
Abstract: The proliferation of advanced mobile devices has enabled the wide-spread adoption of computation-intensive mobile applications. Nevertheless, the execution of these applications is still constrained by the limited battery and processing capacity of the devices. These limitations have led to the mobile cloud/edge computation offloading paradigm, and several such architectures have been proposed in recent years. However, we currently lack a clear understanding of the benefits of these diverse offloading solutions in terms of computation delay and energy cost savings. In this paper, we implement a hierarchical mobile-edge-cloud computing system that is comprised of a mobile device, an edge server and a cloud server, and conduct a series of experiments to measure the device's energy consumption, as well as the computation and transmission delay for different tasks. Our experiments reveal an interesting relation between the mobile's CPU clock frequency and the transmission delay for the offloaded tasks. Based on this finding, we propose a dynamic greedy algorithm that selects the CPU frequency, the tasks to be offloaded, and the network path (cellular or WiFi), in order to reduce the total energy cost and execution delay. We fully implement this multi-tier architecture and verify experimentally that the proposed algorithm can save up to 50% of the device battery energy, at the expense of transmission delay. Our results motivate the design of offloading policies that optimize jointly the CPU clock and network capacity.
External IDs:dblp:conf/wcnc/JosephKI19
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